惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

WordPress大学
WordPress大学
J
Java Code Geeks
Martin Fowler
Martin Fowler
Microsoft Azure Blog
Microsoft Azure Blog
月光博客
月光博客
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
人人都是产品经理
人人都是产品经理
有赞技术团队
有赞技术团队
爱范儿
爱范儿
Engineering at Meta
Engineering at Meta
GbyAI
GbyAI
博客园 - 【当耐特】
Y
Y Combinator Blog
Last Week in AI
Last Week in AI
MongoDB | Blog
MongoDB | Blog
G
Google Developers Blog
博客园 - 三生石上(FineUI控件)
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
大猫的无限游戏
大猫的无限游戏
罗磊的独立博客
The Cloudflare Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
V
V2EX
博客园 - 司徒正美

math.ST updates on arXiv.org

What is Learnable in Valiant's Theory of the Learnable? Learning Perturbations to Extrapolate Your LLM Byzantine-Robust Distributed Sparse Learning Revisited The Sample Complexity of Multiple Change Point Identification under Bandit Feedback A proximal gradient algorithm for composite log-concave sampling Model-based Bootstrap of Controlled Markov Chains Approximation of Maximally Monotone Operators : A Graph Convergence Perspective Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound A Spectral Framework for Closed-Form Relative Density Estimation Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation Scaling Limits of Long-Context Transformers A Note on Non-Negative $L_1$-Approximating Polynomials Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning Linear Response Estimators for Singular Statistical Models Statistical inference with belief functions: A survey Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity Adaptive auditing of AI systems with anytime-valid guarantees Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions Risk-Controlled Post-Processing of Decision Policies Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment Time-Inhomogeneous Preconditioned Langevin Dynamics A Fine-Grained Understanding of Uniform Convergence for Halfspaces CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency Ratio-based Loss Functions Optimal Confidence Band for Kernel Gradient Flow Estimator A renormalization-group inspired lattice-based framework for piecewise generalized linear models
Exponential Hilbert series and hierarchical log-linear mo...
Wayne A. Johnson · 2022-11-08 · via math.ST updates on arXiv.org

Consider a hierarchical log-linear model, given by a simplicial complex, $Γ$, and integer matrix $A_Γ$. We give a new characterization of the rank of $A_Γ$ given by a logarithmic transformation on the exponential Hilbert series of $Γ$. We show that, if each random variable in $X$ has the same number of possible outcomes, then this formula reduces to a simple description in terms of the face vector of $Γ$. If $Γ$ further satisfies the Dehn-Sommerville relations, then we give an exceptionally simple formula for computing the rank of $A_Γ$, and thus the dimension and the number of degrees of freedom of the model.